Influence of Light Waves on the Effective Electron Mass in Quantum Wells, Wires, Inversion Layers and Superlattices

Bose, P. K.; Paitya, Nitai; Bhattacharya, S.; de, Dominique; Saha, Sounik; Chatterjee, Karunamoy; Pahari, S.; Ghatak, K. P.

doi:10.1166/qm.2012.1009

Cited by 27 publications

(7 citation statements)

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“…12 We provides similar utilities as the hyperGTest function in the GO stats package from BioConductor. 3 The main difference is that Our Method simplifies the usage of custom chip-to-gene and gene-to-GO mappings. Every bunch of clustered genes we obtained 6 GO terms, we choose the most Enriched GO term according the p-value and put it into our results.…”

Section: Gene Ontology Analysismentioning

confidence: 99%

“…2 However, there are limited studies on transcriptional profiling of schizophrenia from multiple brain regions and neocortical areas. [3][4][5] Allen carried out a systematically random-effects metaanalyses and have created a regularly updated online * Author to whom correspondence should be addressed. database of all published genetic association studies for schizophrenia.…”

Section: Introductionmentioning

confidence: 99%

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Co-Expressing Patterns of Schizophrenia Candidate Genes in Brain Regions

Lu¹,

Feng²,

Deng³

et al. 2015

j comput theor nanosci

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Transcriptional profiling of human brain will be great helpful understanding the molecular mechanism of schizophrenia. Top 250 schizophrenia candidate genes were analyzed in six brain regions, including frontal lobe, temporal lobe, parietal lobe, basal ganglia, occipital lobe and hippocamel by 2D hieratical clustering method. The clustering results indicated certain gene pathology have similar expression level in some brain sub-regions while different in some others. We found that local pattern of the sample clustering can reflects the cytoarchitecture of basal ganglia, hippocamel and occipital lobe, while in temporal lobe, parieatal lobe and frontal lobe the situation is less discriminable. From the gene clustering pattern, we found that there are some genes have the similar co-expressing level across some brain regions, while in some other regions, the co-expressing level are appears to be very different. Our experimental results strongly proved that local pattern of schizophrenia candidate genes were not only just simply reflecting sub-brain cytoarchitecture but also on functional coordination between each molecular components.

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Section: Gene Ontology Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Co-Expressing Patterns of Schizophrenia Candidate Genes in Brain Regions

Lu¹,

Feng²,

Deng³

et al. 2015

j comput theor nanosci

View full text Add to dashboard Cite

show abstract

“…The original bacterial foraging optimization (BFO) algorithm is one of the state-of-the-art evolutionary algorithms, [13][14][15] which consists of three principal mechanisms, namely chemotaxis, reproduction, and elimination-dispersal. We briefly describe each of these processes as follows:…”

Section: Bfo Modelmentioning

confidence: 99%

Robot Path Planning Using Bacterial Foraging Algorithm

Liu¹,

Niu²,

Chen³

et al. 2013

Jnl of Comp & Theo Nano

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The goal of the robot path planning problem is to determine an optimal collision-free path for a mobile robot between a start and a target point in an environment surrounded by obstacles. Optimal collision-free trajectory planning for mobile robot is always a major issue in robotics due to the necessity for the robots' course of movement. In recent years, as the emergence of another member of the swarm intelligence family-bacterial foraging optimization (BFO), the bacterial foraging strategy has attracted a great deal of interests. In this work, the path planning problem is approached by the mobile robot that mimics the foraging strategy of BFO algorithm. The objective is to minimize the path length and the number of turns without colliding with an obstacle. In the simulation studies, two test scenarios of static environment with different obstacle distribution are adopted to evaluate the performance of the proposed method. Simulation results show that our method is able to generate a collision-free path in complex environment.

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“…2 3 Recently graphene considering has drawn focused due to its exceptional mechanical and electrical properties. [4][5][6][7][8][9] Graphene as two Dimensional (2D) structure is very beneficial to describe the properties of its carbon-based materials such as 0D (fullerenes), 1D i.e., carbon nanotubes (wrapped a graphene sheet in nanomaterial cylindrical shape) and 3D i.e., graphite. 2 10-17 Graphene has been used in many applications, such as batteries, energy storage, catalytic devices and biosensors because its applications show many advantages such as highly conductive, mechanical flexibility, transparent electrodes, low production cost and high surface area.…”

Section: Introductionmentioning

confidence: 99%

Layer Effect on Graphene Nanoribbon Quantum Capacitance

Kiani

Ahmadi

Ravangard

et al. 2013

Jnl of Comp & Theo Nano

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Graphene has amazing carrier transport property and high sensitivity at the single molecule level which leads them as a promising material for batteries, energy storage, catalytic and biosensor applications. The quantum capacitance of different layer graphene is the a notable factor in the nano-electronic device modelling and characterization as a basic electronic parameter. In this work analytical modelling of first sub-band energy for Bilayer Graphene Nanoribbons (BGNS) is employed to model quantum capacitance on Mono Layer Graphene Nanoribbon (MGNR) and BG. Based on the presented model in the non-degenerate regime specifies the temperature effect on quantum capacitance is more dominant while in the degenerate limit constant quantum capacitance is reported which is independent of temperature. According to the analytical model the BGN quantum capacitance in comparison with monolayer GNR quantum capacitance is investigated and BGN higher quantum capacitance is reported.

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Influence of Light Waves on the Effective Electron Mass in Quantum Wells, Wires, Inversion Layers and Superlattices

Cited by 27 publications

References 0 publications

Co-Expressing Patterns of Schizophrenia Candidate Genes in Brain Regions

Co-Expressing Patterns of Schizophrenia Candidate Genes in Brain Regions

Robot Path Planning Using Bacterial Foraging Algorithm

Layer Effect on Graphene Nanoribbon Quantum Capacitance

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